Conventional road tracking approaches rely on direct measurements of the road. Using camera, LiDAR, or radar sensors, they detect lane markings, textures, road boundaries, curbs, or obstacles. However, all require a direct line of sight to the road. In shared spaces, e.g., a busy campus road, a lot of pedestrian groups are occluding the road for all sensors. Hence, we present a novel measurement concept to support road tracking in presence of other road users. In fact, we are taking advantage of them: Where other road users are moving, there might be the road. Therefore, we probabilistically identify dynamic objects based on radar Doppler measurements. Considering the measurement uncertainties, we then build a probability map of road user paths. This is used to derive probable road areas. Integrating this measurement into an existing camera-based road tracking, we evaluate the accuracy of the resulting road estimate using an HD map.

Stereo matching can be used to estimate dense but inaccurate depth information for each pixel of a camera image. A LiDAR can provide accurate but sparse depth measurements. The fusion of both can combine their advantages. We propose an efficient method for fusing stereo and LiDAR at the cost level of Semi-Global Matching. It significantly improves density and accuracy of the estimated disparities while remaining real-time capable. Based on a LiDAR point cloud projected into the camera image costs are calculated for each possible disparity. These costs are added to the costs from stereo matching. Our LiDAR-SGM outperforms other real-time capable fusion approaches evaluated on the KITTI Stereo 2015 dataset. In addition to this real data, synthetic datasets are created (and made available) for a detailed analysis of the benefit of stereo LiDAR fusion as well as the evaluation of different sensors.

For autonomous driving on rural or dirt roads-neither urban nor off-road - a large terrain area needs to be estimated at high spatial resolution. However, available computing time is very limited. Since different areas of the ground surface require different minimum resolution, we propose a dynamic resolution terrain estimation.Based on support points, accumulated measurements are spatially smoothed to a continuous terrain model using maximum a posteriori estimation. Splitting the terrain into tiles, we dynamically adjust the support point resolution of single tiles, depending on their accuracy in areas of interest. Areas of interest are determined by fusing information on probable road areas from LiDAR and vision preprocessing steps.As demonstrated in real-world examples, our approach can model the terrain almost as accurately as if all tiles had the highest resolution, but with much less computational effort.

For autonomous driving in urban environments it is usually assumed that the road is flat. To drive off-road, however, we need a more sophisticated model of the ground surface. While previous work is mapping the terrain along with static obstacles, we propose to separate the tasks and introduce a new approach to probabilistic terrain estimation. It combines recursive Gaussian state estimation with a subsequent maximum a posteriori estimation. This allows us to efficiently accumulate obtained measurements and at the same time get a probabilistic terrain estimate based on a geometric terrain model. This way, also (measurement) uncertainties as well as inter- and extrapolation to unobserved areas are handled stochastically correct. We demonstrate the effectiveness and real-time capability of our approach using real-world data.

We propose a modular framework for 3D tracking not only of paved roads but also of dirt roads. It is based on recursive state estimation of lane boundary points connected by clothoid pieces. While our tracking is flexible to integrate every kind of measurement, we specifically propose two image-based measurements. They combine traditional with modern computer vision: On the one hand, we show how to use directed edge detection to robustly measure road and lane boundaries. On the other hand, we introduce a innovative CNN-based measurement utilizing the self-similarity of (dirt) road areas. We demonstrate the performance of our approach in challenging scenarios. On a marked road, we achieve a median error of 0.13 m for the ego lane's boundaries in 25 m look-ahead. A difficult dirt road can also be tracked reliably with a lookahead length of 25 m, resulting in a median error of 0.3 m. The tracking, as well as both measurements, are real-time capable.

Fiducial markers like AprilTags play an important role in robotics, e.g., for the calibration of cameras or the localization of robots. One of the most important properties of an algorithm for detecting such tags is its localization accuracy.In this paper, we present the results of an extensive comparison of four freely available libraries capable of detecting AprilTags, namely AprilTag 3, AprilTags C++, ArUco as standalone libraries, and the OpenCV algorithm based on ArUco. The focus of the comparison is on localization accuracy, but the processing time is also examined. Besides working with pure tags, their extension to checkerboard corners is investigated.In addition, we present two new post-processing techniques. Firstly, a method that can filter out very inaccurate detections resulting from partial border occlusion, and secondly a new highly accurate method for edge refinement. With this we achieve a median pixel error of 0.017 px, compared to 0.17 px for standard OpenCV corner refinement.The dataset used for the evaluation, as well as the developed post-processing techniques, are made publicly available to encourage further comparison and improvement of the detection libraries.

In this work, we propose an extension of the Semi-Global Matching framework for three images from a triplet-stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separately for both image pairs, these are merged at cost level using cubic spline interpolation. For cost values near the left/bottom image boundaries, we propose an advanced weighting strategy. Subsequently, the fused matching can be used directly for the cost aggregation and disparity estimation.The benefits of the proposed fusion strategy are demonstrated by an evaluation based on synthetic and real-world data. To encourage further comparisons on triple stereo algorithms, the dataset used for evaluation is made publicly available.